In this work, we introduce a detection scheme that is able to identify regions of interest during the intermediate stages of an image formation process for ultra-wideband (UWB) synthetic aperture radar. Traditional detection methods manipulate the data after image formation. However, this approach wastes computational resources by resolving to completion the entire scene including area dominated by benign clutter. As an alternative, we introduce a multiscale focus of attention (FOA) algorithm that processes intermediate radar data from a quadtree-based backprojection image formation algorithm. As the stages of the quadtree algorithm progress, the FOA thresholds a detection statistic that estimates the signal-to-background ratio for increasingly smaller subpatches. Whenever a subpatch fails a detection, the FOA cues the image formation processor to terminate further processing of that subpatch. We demonstrate that the FOA is able to decrease the overall computational load of the image formation process by a factor of two. We also show that the new FOA method provides fewer false alarms than the two-parameter CFAR FOA over a small database of UWB radar data.